{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tigress data exclusive processing\n",
    "This is only necessary for the CMIP data, which resides on the princeton machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import xesmf as xe\n",
    "from glob import glob\n",
    "from xarrayutils import concat_dim_da\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_equator_full(ds, field='o2'):\n",
    "    ds_target = xr.Dataset({'lat': (['lat'], np.arange(-5, 5, 0.5)),\n",
    "                     'lon': (['lon'], np.arange(-270, -60, 1)),\n",
    "                    }\n",
    "                   )\n",
    "    ds = ds.copy()\n",
    "    print(ds.attrs['model_id'])\n",
    "    if len(ds.lon.shape) == 1:\n",
    "        lon, lat = np.meshgrid(ds.lon, ds.lat)\n",
    "        ds = ds.rename({'lon':'rlon', 'lat':'rlat'})\n",
    "        \n",
    "        llon = xr.DataArray(lon, dims=['rlat', 'rlon'])\n",
    "        llat = xr.DataArray(lat, dims=['rlat', 'rlon'])\n",
    "        ds.coords['lon'] = llon\n",
    "        ds.coords['lat'] = llat\n",
    "\n",
    "    regridder = xe.Regridder(ds, ds_target, 'bilinear')\n",
    "    out = regridder(ds[field])\n",
    "    regridder.clean_weight_file()\n",
    "    return out\n",
    "# So this is not ideal..I need to rename the coords to dimensions and then average over 1S-1N\n",
    "\n",
    "# Check if all files are there\n",
    "def filecheck(path):\n",
    "    li = glob(path)\n",
    "    if len(li) == 1:\n",
    "        return li[0]\n",
    "    elif len(li) == 0:\n",
    "        raise RuntimeError('No files found for %s' %(path))\n",
    "    else:\n",
    "        raise RuntimeError('Multiple files found: %s' %li)\n",
    "        \n",
    "\n",
    "def replace_time(ds, reftime):\n",
    "    ds = ds.copy()\n",
    "\n",
    "#     if len(ds.time)>=len(reftime):\n",
    "#         ds['time'].data = reftime[0:len(reftime)]\n",
    "#     else:\n",
    "    ds['time'].data = reftime[0:len(ds.time)]\n",
    "#     print(ds.time[0])\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCENARIO:piControl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NorESM1-ME\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n",
      "CESM1-BGC\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPI-ESM-LR\n",
      "Create weight file: bilinear_220x256_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_220x256_20x210.nc\n",
      "GFDL-ESM2M\n",
      "Create weight file: bilinear_200x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_200x360_20x210.nc\n",
      "IPSL-CM5A-LR\n",
      "Create weight file: bilinear_85x182_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_85x182_20x210.nc\n",
      "GFDL-ESM2G\n",
      "Create weight file: bilinear_210x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_210x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPI-ESM-MR\n",
      "Create weight file: bilinear_404x802_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_404x802_20x210.nc\n",
      "NorESM1-ME\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n",
      "CESM1-BGC\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPI-ESM-LR\n",
      "Create weight file: bilinear_220x256_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_220x256_20x210.nc\n",
      "GFDL-ESM2M\n",
      "Create weight file: bilinear_200x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_200x360_20x210.nc\n",
      "IPSL-CM5A-LR\n",
      "Create weight file: bilinear_85x182_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_85x182_20x210.nc\n",
      "GFDL-ESM2G\n",
      "Create weight file: bilinear_210x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_210x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPI-ESM-MR\n",
      "Create weight file: bilinear_404x802_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_404x802_20x210.nc\n",
      "<xarray.Dataset>\n",
      "Dimensions:  (lat: 20, lev: 70, lon: 210, model: 7, time: 100)\n",
      "Coordinates:\n",
      "  * model    (model) <U12 'NorESM1-ME' 'CESM1-BGC' ... 'GFDL-ESM2G' 'MPI-ESM-MR'\n",
      "  * lon      (lon) int64 -270 -269 -268 -267 -266 -265 ... -65 -64 -63 -62 -61\n",
      "  * lat      (lat) float64 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 ... 2.5 3.0 3.5 4.0 4.5\n",
      "  * time     (time) object 2006-07-01 00:00:00 ... 2105-07-01 00:00:00\n",
      "  * lev      (lev) float64 0.0 5.0 10.0 15.0 ... 6e+03 6.25e+03 6.5e+03 6.75e+03\n",
      "Data variables:\n",
      "    o2       (model, time, lev, lat, lon) float64 0.2031 0.203 ... nan nan\n",
      "    uo       (model, time, lev, lat, lon) float64 0.1483 0.1436 ... nan nan\n",
      "SCENARIO:rcp85\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/tiger/scratch/gpfs/jbusecke/projects/global_oxygen/src/xarray/xarray/coding/times.py:419: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  self.use_cftime)\n",
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NorESM1-ME\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n",
      "CESM1-BGC\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPI-ESM-LR\n",
      "Create weight file: bilinear_220x256_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_220x256_20x210.nc\n",
      "GFDL-ESM2M\n",
      "Create weight file: bilinear_200x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_200x360_20x210.nc\n",
      "MRI-ESM1\n",
      "Create weight file: bilinear_250x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_250x360_20x210.nc\n",
      "IPSL-CM5A-LR\n",
      "Create weight file: bilinear_85x182_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_85x182_20x210.nc\n",
      "GFDL-ESM2G\n",
      "Create weight file: bilinear_210x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_210x360_20x210.nc\n",
      "MPI-ESM-MR\n",
      "Create weight file: bilinear_404x802_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_404x802_20x210.nc\n",
      "NorESM1-ME\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n",
      "CESM1-BGC\n",
      "Create weight file: bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_384x320_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/numpy/core/numeric.py:501: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using cftime.datetime objects instead, reason: dates out of range\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MPI-ESM-LR\n",
      "Create weight file: bilinear_220x256_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_220x256_20x210.nc\n",
      "GFDL-ESM2M\n",
      "Create weight file: bilinear_200x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_200x360_20x210.nc\n",
      "MRI-ESM1\n",
      "Create weight file: bilinear_250x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_250x360_20x210.nc\n",
      "IPSL-CM5A-LR\n",
      "Create weight file: bilinear_85x182_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_85x182_20x210.nc\n",
      "GFDL-ESM2G\n",
      "Create weight file: bilinear_210x360_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_210x360_20x210.nc\n",
      "MPI-ESM-MR\n",
      "Create weight file: bilinear_404x802_20x210.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbusecke/code/miniconda/envs/global_oxygen/lib/python3.6/site-packages/xesmf/smm.py:73: UserWarning: Input array is not C_CONTIGUOUS. Will affect performance.\n",
      "  warnings.warn(\"Input array is not C_CONTIGUOUS. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Remove file bilinear_404x802_20x210.nc\n",
      "<xarray.Dataset>\n",
      "Dimensions:  (lat: 20, lev: 70, lon: 210, model: 8, time: 295)\n",
      "Coordinates:\n",
      "  * model    (model) <U12 'NorESM1-ME' 'CESM1-BGC' ... 'GFDL-ESM2G' 'MPI-ESM-MR'\n",
      "  * time     (time) object 2006-07-01 00:00:00 ... 2300-07-01 00:00:00\n",
      "  * lon      (lon) int64 -270 -269 -268 -267 -266 -265 ... -65 -64 -63 -62 -61\n",
      "  * lat      (lat) float64 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 ... 2.5 3.0 3.5 4.0 4.5\n",
      "  * lev      (lev) float64 0.0 5.0 10.0 15.0 ... 6e+03 6.25e+03 6.5e+03 6.75e+03\n",
      "Data variables:\n",
      "    o2       (model, time, lev, lat, lon) float64 0.1995 0.1998 ... nan nan\n",
      "    uo       (model, time, lev, lat, lon) float64 0.2497 0.2446 ... nan nan\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = '/tigress/laurer/CMIP5'\n",
    "\n",
    "for scenario in ['piControl', 'rcp85', ]:\n",
    "    print('SCENARIO:%s' %scenario)\n",
    "    opath = '../data/processed/CMIP5_tropical_combined_%s.nc' %scenario\n",
    "    filelist = glob(path+'/cmip*')\n",
    "    models = list(set([a.replace(path,'').split('.')[3] for a in filelist]))\n",
    "    \n",
    "    # there is no piControl fir MRI\n",
    "    if scenario == 'piControl':\n",
    "        models = [m for m in models if not 'MRI' in m]\n",
    "\n",
    "    # for now I am missing the HadGEM and Miroc\n",
    "    models = [m for m in models if ('HadGEM' not in m) and ('MIROC' not in m)]\n",
    "\n",
    "    uo_files = [filecheck('%s/cmip5.merge.*.%s.%s.yr.*uo*nc' %(path, m, scenario)) for m in models]\n",
    "    o2_files = [filecheck('%s/cmip5.merge.*.%s.%s.yr.*o2*nc' %(path, m, scenario)) for m in models]\n",
    "\n",
    "    raw_uo_datasets = [xr.open_mfdataset(path) for path in uo_files]\n",
    "    raw_o2_datasets = [xr.open_mfdataset(path) for path in o2_files]\n",
    "\n",
    "    ############ interpolate all datasets onto reference model (take one 300 yr one)\n",
    "    # time_ref = uo_datasets['IPSL-CM5A-LR'].time\n",
    "    # Ok this is HELL! I will replace all calendars assuming they start in 2006 and represent yearly averages\n",
    "    # I CANNOT SAVE THIS AAAEEERRRHGGGHFGHGHGHFGHGHGHGHG so i will restrict it to a valid \n",
    "    time_ref = xr.cftime_range(start='2006', end='2301', freq='AS-JUL', calendar='standard')#.to_datetimeindex()\n",
    "\n",
    "    # parse to dict\n",
    "    uo_datasets = {k:v for k,v in zip(models, raw_uo_datasets)}\n",
    "    o2_datasets = {k:v for k,v in zip(models, raw_o2_datasets)}\n",
    "\n",
    "    # precut the weird french model grid\n",
    "    if 'IPSL-CM5A-LR' in o2_datasets.keys():\n",
    "        o2_datasets['IPSL-CM5A-LR'] = o2_datasets['IPSL-CM5A-LR'].sel(y=slice(0,85))\n",
    "        uo_datasets['IPSL-CM5A-LR'] = uo_datasets['IPSL-CM5A-LR'].sel(y=slice(0,85))\n",
    "\n",
    "    if 'MRI-ESM1' in o2_datasets.keys():\n",
    "        o2_datasets['MRI-ESM1'] = o2_datasets['MRI-ESM1'].sel(y=slice(0,250))\n",
    "        uo_datasets['MRI-ESM1'] = uo_datasets['MRI-ESM1'].sel(y=slice(0,250))\n",
    "\n",
    "    ############# interpolate everything on a common vertical grid\n",
    "    ref_lev = o2_datasets['NorESM1-ME'].lev\n",
    "\n",
    "    o2_datasets = {k:replace_time(v.interp(lev=ref_lev),\n",
    "                                           time_ref) for k,v in o2_datasets.items()}\n",
    "    uo_datasets = {k:replace_time(v.interp(lev=ref_lev),\n",
    "                                           time_ref) for k,v in uo_datasets.items()}\n",
    "\n",
    "    # Process the datasets:\n",
    "    uo_datasets_processed = {kk:fetch_equator_full(dd.load(), field='uo') for kk, dd in uo_datasets.items()}\n",
    "    o2_datasets_processed = {kk:fetch_equator_full(dd.load(), field='o2') for kk, dd in o2_datasets.items()}\n",
    "\n",
    "\n",
    "    datasets_combined = {k:xr.Dataset({'o2':o2_datasets_processed[k].copy().interp(lev=ref_lev),\n",
    "                                       'uo':uo_datasets_processed[k].copy().interp(lev=ref_lev)}) for k in o2_datasets_processed.keys()}\n",
    "\n",
    "    mod_names = list(datasets_combined.keys())\n",
    "    mod_ds = [datasets_combined[k] for k in mod_names]\n",
    "    ds = xr.concat(mod_ds, concat_dim_da(mod_names, 'model'))\n",
    "    print(ds)\n",
    "    ds.attrs['scenario'] = scenario\n",
    "\n",
    "    ds.o2.isel(time=10, lev=12).plot(col='model', col_wrap=4)\n",
    "    ds.to_netcdf(opath)"
   ]
  }
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